诊断学理论与实践

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2018 , Vol. 17 >Issue 06: 694 - 700

DOI: https://doi.org/10.16150/j.1671-2870.2018.06.013

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基于癌症基因图谱挖掘前列腺癌不同Gleason分级癌组织相关基因分析

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  • 1.上海交通大学附属第一人民医院泌尿外科,上海 200080;
    2.复旦大学附属肿瘤医院病理科,上海 200030;
    3.上海交通大学医学院附属瑞金医院病理科,上海 200025

收稿日期: 2018-09-29

  网络出版日期: 2018-12-25

基金资助

上海市卫生计生委委级科研项目（201540280）

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Identification of genes associated with distinguished Gleason patterns of prostate cancer by analyzing TCGA database

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  • 1. Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai 200080, China;
    2. Department of Pathology, Shanghai Cancer Center, Fudan University, Shanghai 200030, China;
    3. Department of Pathology, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China

Received date: 2018-09-29

  Online published: 2018-12-25

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摘要

目的：运用生物信息学探究不同Gleason分级（Gleason Pattern,GP）前列腺癌组织间的分子表达差异及其基因间的相互影响。方法：下载癌症基因图谱（the cancer genome atlas,TCGA）前列腺癌患者的转录组数据及GP资料,运用生物信息学分析GP 4级与GP 3级前列腺癌组织间的基因表达差异,进而通过基因本体（gene ontology, GO）、京都基因和基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）信号通路富集及蛋白相互作用网络（protein protein interaction network, PPI）,探究不同GP的前列腺癌组织间差异表达基因的相互作用及影响的生物学过程。结果：生信分析共鉴定出312个差异表达基因,且相较于GP 3级癌组织,GP 4级前列腺癌组织中存在157个基因表达上调,155个基因表达下调。生信分析显示,在基因相互作用网络中富集到的22个显著相关基因主要参与的生物学过程为细胞周期及有丝分裂。结论：GP 4级前列腺癌组织内的肿瘤细胞有丝分裂更为活跃,临床可通过检测细胞周期相关蛋白,协助对前列腺癌患者作出更合理的疾病风险及预后评估;同时,针对Gleason积分（Gleason score,GS）≥7分的患者,细胞周期相关蛋白有望成为其有效的治疗靶点。

关键词： 前列腺癌; Gleason评分; 癌症基因图谱; 生物信息学; 细胞周期

本文引用格式

王涛, 邓玉, 赵萍, 于宝华, 王翔, 王朝夫 . 基于癌症基因图谱挖掘前列腺癌不同Gleason分级癌组织相关基因分析[J]. 诊断学理论与实践, 2018 , 17(06) : 694 -700 . DOI: 10.16150/j.1671-2870.2018.06.013

Abstract

Objective: To investigate the differentially expressed genes involved in distinguished Gleason patterns of prostate cancer (PC) and to reveal the potential molecular mechanisms by applying integrated bioinformatics. Methods: The expression profiles and information for Gleason score were downloaded from the cancer genome atlas (TCGA) database. The differentially expressed genes (DEGs) were obtained and were further analyzed for their interaction and biological processes involved by bioinformatics methods. The gene ontology(GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were used for processing by DAVID online analyses. The protein-protein interaction (PPI) networks of the DEGs were constructed from the STRING database. Results: A total of 312 DEGs were identified in the TCGA datasets, of which 157 genes were upregulated and 155 genes were downregulated, in the Gleason pattern 4 PC. The 22 most closely related genes among DEGs were identified from the PPI network and were focused primarily on cell cycle and mitotic division. Conclusions: This study indicated that cancer cells in Gleason pattern 4 PC are more dynamic in the process of mitotic cell cycle. In clinical practice, detection of protein associated with cell cycle may allow the more rational prediction for risk and prognosis of PC. Meanwhile, for patients with Gleason score ≥7 PC, genes associated with cell cycle may provide the effective targets for treatment of prostate cancer.

Key words： Prostate cancer; Gleason score; The cancer genome atlas; Bioinformatics; Cell cycle

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